RT Journal Article
SR Electronic
T1 Development and validation of prediction models for incident atrial fibrillation in heart failure
JF Open Heart
JO Open Heart
FD British Cardiovascular Society
SP e002169
DO 10.1136/openhrt-2022-002169
VO 10
IS 1
A1 Nicklas Vinter
A1 Thomas Alexander Gerds
A1 Pia Cordsen
A1 Jan Brink Valentin
A1 Gregory Y H Lip
A1 Emelia J J Benjamin
A1 Søren Paaske Johnsen
A1 Lars Frost
YR 2023
UL http://openheart.bmj.com/content/10/1/e002169.abstract
AB Objectives Accurate prediction of heart failure (HF) patients at high risk of atrial fibrillation (AF) represents a potentially valuable tool to inform shared decision making. No validated prediction model for AF in HF is currently available. The objective was to develop clinical prediction models for 1-year risk of AF.Methods Using the Danish Heart Failure Registry, we conducted a nationwide registry-based cohort study of all incident HF patients diagnosed from 2008 to 2018 and without history of AF. Administrative data sources provided the predictors. We used a cause-specific Cox regression model framework to predict 1-year risk of AF. Internal validity was examined using temporal validation.Results The population included 27 947 HF patients (mean age 69 years; 34% female). Clinical experts preselected sex, age at HF, NewYork Heart Association (NYHA) class, hypertension, diabetes mellitus, chronic kidney disease, obstructive sleep apnoea, chronic obstructive pulmonary disease and myocardial infarction. Among patients aged 70 years at HF, the predicted 1-year risk was 9.3% (95% CI 7.1% to 11.8%) for males and 6.4% (95% CI 4.9% to 8.3%) for females given all risk factors and NYHA III/IV, and 7.5% (95% CI 6.7% to 8.4%) and 5.1% (95% CI 4.5% to 5.8%), respectively, given absence of risk factors and NYHA class I. The area under the curve was 65.7% (95% CI 63.9% to 67.5%) and Brier score 7.0% (95% CI 5.2% to 8.9%).Conclusion We developed a prediction model for the 1-year risk of AF. Application of the model in routine clinical settings is necessary to determine the possibility of predicting AF risk among patients with HF more accurately and if so, to quantify the clinical effects of implementing the model in practice.Data may be obtained from a third party and are not publicly available. Permission to access the data used on this study can be obtained following approval from the Danish Health Authority.